Consider the problem of exploring a large state-space for a goal
state where although many such states may exist in the state-space,
finding any one state satisfying the requirements is sufficient.
All the methods known until now for conducting such search in
parallel using multiprocessors fail to provide consistent linear
speedups over sequential execution. The speedups vary between
sublinear to superlinear and from one execution to another.
Further, adding more processors may sometimes lead to a slow-down
rather than speedup, giving rise to speedup anomalies reported in
literature. We present a prioritizing strategy which yields
consistent speedups that are close toP withP processors, and that
monotonically increase with the additon of processors. This is
achieved by keeping the total number of nodes expanded during
parallel search very close to that of a sequential search. In
addition, the strategy requires substantially smaller memory
relative to other methods. The performance of this strategy is
demonstrated on a multiprocessor with several state-space search
problems.
This paper can be found on SpringerLink